Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Microsatellite Constellation for Disaster Monitoring

Every year natural and manmade disasters cause devastation around the World through loss of life, widespread human suffering, and huge economic losses. Remote sensing satellites can contribute to mitigation of this devastation through early warning, event monitoring, and after-theevent studies. Unfortunately, present satellite remote sensing systems do not provide the high temporal resolution required for this activity. Additionally, the images they provide come at high cost per scene. The Surrey Space Centre at the University of Surrey has designed a constellation of remote sensing micro satellites that delivers 35 m ground resolution over a 600 km width scene in up to four spectral bands. Cost-benefit tradeoffs show that such images can fulfil many needs with the disaster monitoring community. However, spatial and spectral resolution are not the primary requirements for disaster monitoring; Disaster monitoring users demand high temporal resolution. Emerging manmade or natural disasters must be monitored on a daily basis if mitigation efforts are to be effective. Low-cost microsatellites applied in large constellations provide the only cost-effective solution to this design driver. This paper reports the details of Surrey\u27s Disaster Monitoring Constellation, describing the key subsystem technologies which deliver the desired price/performance ratio, and the overall system design which exploits the low unit cost of micro satellites to deliver a large constellation in affordable and useful increments

Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery

Many significant applications need land cover information of remote sensing images that are acquired from different areas and times, such as change detection and disaster monitoring. However, it is difficult to find a generic land cover classification scheme for different remote sensing images due to the spectral shift caused by diverse acquisition condition. In this paper, we develop a novel land cover classification method that can deal with large-scale data captured from widely distributed areas and different times. Additionally, we establish a large-scale land cover classification dataset consisting of 150 Gaofen-2 imageries as data support for model training and performance evaluation. Our experiments achieve outstanding classification accuracy compared with traditional methods.Comment: IGARSS'18 conference pape

Adaptive service discovery on service-oriented and spontaneous sensor systems

Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief